Machine Learning-based MIMO Indoor Positioning
Abstract: The most widely used positioning system is Global Navigation Satellite System (GNSS), which uses traditional positioning techniques and cannot achieve satisfactory positioning performance in indoor scenarios due to Non-Line-of-Sight (NLoS) transmission. Fingerprinting is a non-traditional positioning technique that is robust to NLoS transmission in indoor scenarios. Moreover, Applying Machine Learning (ML) to fingerprinting positioning can significantly improve positioning performance. Therefore the main objective of this project is to investigate the effect of different Multi-Input Multi-Output (MIMO) antenna topologies, the number of MIMO antennas, ML algorithms, and Channel State Information (CSI) fingerprints on the performance of ML-based fingerprinting positioning. The four open-source datasets used for investigation were measured on the Massive MIMO testbed of ESAT-TELEMIC at KU Leuven. Three datasets were collected when Uniform Rectangular Array (URA), Uniform Linear Array (ULA), and Distributed ULAs as Base Station (BS) under Line-of-sight (LoS) transmission, and one dataset was collected on URA BS under NLoS transmission. The antenna topologies studied in this project are three 64-antenna topologies and five 8-antenna topologies. The ML algorithms studied are Support Vector Regression (SVR), Fully Connected Neural Network (FCNN), and Convolutional Neural Network (CNN). The fingerprints studied are Channel Impulse Response (CIR) and Channel Frequency Response (CFR). The number of antennas studied is 8-antenna ULA, 16-antenna ULA, and 32-antenna ULA. The positioning error measures the fingerprinting performance, which is the Euclidean distance between the predicted and ground truth coordinates. All comparisons are presented using the empirical Cumulative Distribution Function (CDF) curves of the positioning error. The investigation results show that increasing the number of antennas of ULA improves positioning performance. CIR fingerprints and CFR fingerprints have comparable positioning performance, 64-antenna URA has the best positioning performance, and the 8-antenna random array has the best positioning performance. The two Deep Neural Networks (DNNs), FCNN and CNN, have much better positioning performance than the traditional ML algorithm, SVR. However, the difference between the positioning performance of the two DNNs is negligible.
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